{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FhGuhbZ6M5tl"
      },
      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "cellView": "form",
        "id": "AwOEIRJC6Une"
      },
      "outputs": [],
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "cellView": "form",
        "id": "KyPEtTqk6VdG"
      },
      "outputs": [],
      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EIdT9iu_Z4Rb"
      },
      "source": [
        "# Basic regression: Predict fuel efficiency"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bBIlTPscrIT9"
      },
      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td><a target=\"_blank\" href=\"https://tensorflow.google.cn/tutorials/keras/regression\"><img src=\"https://tensorflow.google.cn/images/tf_logo_32px.png\">在 TensorFlow.org上查看</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/regression.ipynb\"><img src=\"https://tensorflow.google.cn/images/colab_logo_32px.png\">在 Google Colab 中运行</a></td>\n",
        "  <td><a target=\"_blank\" href=\"https://github.com/tensorflow/docs-l10n/blob/master/site/zh-cn/tutorials/keras/regression.ipynb\"><img src=\"https://tensorflow.google.cn/images/GitHub-Mark-32px.png\">在 GitHub 上查看源代码</a></td>\n",
        "  <td><a href=\"https://storage.googleapis.com/tensorflow_docs/docs-l10n/site/zh-cn/tutorials/keras/regression.ipynb\"><img src=\"https://tensorflow.google.cn/images/download_logo_32px.png\">下载笔记本</a></td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AHp3M9ZmrIxj"
      },
      "source": [
        "Note: 我们的 TensorFlow 社区翻译了这些文档。因为社区翻译是尽力而为， 所以无法保证它们是最准确的，并且反映了最新的 [官方英文文档](https://tensorflow.google.cn/?hl=en)。如果您有改进此翻译的建议， 请提交 pull request 到 [tensorflow/docs](https://github.com/tensorflow/docs) GitHub 仓库。要志愿地撰写或者审核译文，请加入 [docs-zh-cn@tensorflow.org Google Group](https://groups.google.com/a/tensorflow.org/forum/#!forum/docs-zh-cn)。\n",
        "\n",
        "此教程使用经典的 [Auto MPG](https://archive.ics.uci.edu/ml/datasets/auto+mpg) 数据集并演示了如何构建模型来预测 20 世纪 70 年代末和 20 世纪 80 年代初汽车的燃油效率。为此，您需要为模型提供该时期的许多汽车的描述。这种描述包括诸如气缸、排量、马力和重量等特性。\n",
        "\n",
        "此示例使用了 Keras API。（请访问 Keras [教程](https://tensorflow.google.cn/tutorials/keras)和[指南](https://tensorflow.google.cn/guide/keras)以了解更多信息。）"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 60,
      "metadata": {
        "id": "moB4tpEHxKB3"
      },
      "outputs": [],
      "source": [
        "# Use seaborn for pairplot.\n",
        "# 教程 https://www.runoob.com/matplotlib/seaborn-tutorial.html\n",
        "!pip install -q seaborn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 61,
      "metadata": {
        "id": "1rRo8oNqZ-Rj"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "# Make NumPy printouts easier to read.\n",
        "np.set_printoptions(precision=3, suppress=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 62,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9xQKvCJ85kCQ",
        "outputId": "a30278c3-96d7-4ecc-cebf-e4a4b3b755ae"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2.15.0\n"
          ]
        }
      ],
      "source": [
        "import tensorflow as tf\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "print(tf.__version__)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F_72b0LCNbjx"
      },
      "source": [
        "## Auto MPG 数据集\n",
        "\n",
        "该数据集可以从 [UCI机器学习库](https://archive.ics.uci.edu/ml/) 中获取.\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gFh9ne3FZ-On"
      },
      "source": [
        "### 获取数据\n",
        "\n",
        "首先下载数据集。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 63,
      "metadata": {
        "id": "CiX2FI4gZtTt"
      },
      "outputs": [],
      "source": [
        "url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/auto-mpg/auto-mpg.data'\n",
        "column_names = ['MPG', 'Cylinders', 'Displacement', 'Horsepower', 'Weight',\n",
        "                'Acceleration', 'Model Year', 'Origin']\n",
        "\n",
        "raw_dataset = pd.read_csv(url, names=column_names,\n",
        "                          na_values='?', comment='\\t',\n",
        "                          sep=' ', skipinitialspace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "2oY3pMPagJrO",
        "outputId": "89959cba-6a4f-48d6-857b-3f51ab419801"
      },
      "outputs": [
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              "\n",
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              "    (() => {\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     Model Year  Origin  \n",
              "393          82       1  \n",
              "394          82       2  \n",
              "395          82       1  \n",
              "396          82       1  \n",
              "397          82       1  "
            ]
          },
          "execution_count": 64,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = raw_dataset.copy()\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3MWuJTKEDM-f"
      },
      "source": [
        "### 数据清洗\n",
        "\n",
        "数据集包含一些未知值："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 65,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JEJHhN65a2VV",
        "outputId": "5c4ae709-c404-4398-fa04-9074bfdf3960"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "MPG             0\n",
              "Cylinders       0\n",
              "Displacement    0\n",
              "Horsepower      6\n",
              "Weight          0\n",
              "Acceleration    0\n",
              "Model Year      0\n",
              "Origin          0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 65,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset.isna().sum()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UPN0KBHa_WI"
      },
      "source": [
        "为了保证此初始教程简单，请删除这些行："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 66,
      "metadata": {
        "id": "4ZUDosChC1UN"
      },
      "outputs": [],
      "source": [
        "dataset = dataset.dropna()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8XKitwaH4v8h"
      },
      "source": [
        "`\"Origin\"` 列为分类数据，而不是数值数据。因此，下一步是使用 [pd.get_dummies](https://pandas.pydata.org/docs/reference/api/pandas.get_dummies.html) 对列中的值进行独热编码。\n",
        "\n",
        "注：您可以设置 `tf.keras.Model` 来为您执行这种转换，但这超出了本教程的范围。有关示例，请参阅[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)或[加载 CSV 数据](../load_data/csv.ipynb)教程。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 67,
      "metadata": {
        "id": "gWNTD2QjBWFJ"
      },
      "outputs": [],
      "source": [
        "dataset['Origin'] = dataset['Origin'].map({1: 'USA', 2: 'Europe', 3: 'Japan'})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 68,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 244
        },
        "id": "ulXz4J7PAUzk",
        "outputId": "763cced0-2432-4126-972a-d6a13ca98b24"
      },
      "outputs": [
        {
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              "    </div>\n",
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            ],
            "text/plain": [
              "      MPG  Cylinders  Displacement  Horsepower  Weight  Acceleration  \\\n",
              "393  27.0          4         140.0        86.0  2790.0          15.6   \n",
              "394  44.0          4          97.0        52.0  2130.0          24.6   \n",
              "395  32.0          4         135.0        84.0  2295.0          11.6   \n",
              "396  28.0          4         120.0        79.0  2625.0          18.6   \n",
              "397  31.0          4         119.0        82.0  2720.0          19.4   \n",
              "\n",
              "     Model Year  Europe  Japan    USA  \n",
              "393          82   False  False   True  \n",
              "394          82    True  False  False  \n",
              "395          82   False  False   True  \n",
              "396          82   False  False   True  \n",
              "397          82   False  False   True  "
            ]
          },
          "execution_count": 68,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "dataset = pd.get_dummies(dataset, columns=['Origin'], prefix='', prefix_sep='')\n",
        "dataset.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Cuym4yvk76vU"
      },
      "source": [
        "### 将数据拆分为训练集和测试集\n",
        "\n",
        "现在，将数据集拆分为训练集和测试集。您将在模型的最终评估中使用测试集。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 69,
      "metadata": {
        "id": "qn-IGhUE7_1H"
      },
      "outputs": [],
      "source": [
        "train_dataset = dataset.sample(frac=0.8, random_state=0)\n",
        "test_dataset = dataset.drop(train_dataset.index)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J4ubs136WLNp"
      },
      "source": [
        "### 数据检查\n",
        "\n",
        "检查训练集中几对列的联合分布。\n",
        "\n",
        "第一行表明燃油效率 (MPG) 是所有其他参数的函数。其他行表示它们是彼此的函数。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 70,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 688
        },
        "id": "oRKO_x8gWKv-",
        "outputId": "32ec6e0b-f76e-4720-897c-624c5fa58c9f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7945f84e5720>"
            ]
          },
          "execution_count": 70,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.pairplot(train_dataset[['MPG', 'Cylinders', 'Displacement', 'Weight']], diag_kind='kde')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gavKO_6DWRMP"
      },
      "source": [
        "让我们也查看一下总体统计信息。请注意每个特征覆盖大为不同的范围："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 289
        },
        "id": "yi2FzC3T21jR",
        "outputId": "43c4e434-1493-4cba-9a05-02e89419d1f8"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"train_dataset\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"count\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 314.0,\n        \"max\": 314.0,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          314.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1105.7051185970897,\n        \"min\": 5.477707006369426,\n        \"max\": 2990.251592356688,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          23.31050955414013\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 311.20547986714973,\n        \"min\": 1.6997875727498173,\n        \"max\": 843.8985961905663,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7.72865199891648\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"min\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 611.0058451906518,\n        \"min\": 3.0,\n        \"max\": 1649.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          10.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"25%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 835.5146177940989,\n        \"min\": 4.0,\n        \"max\": 2256.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          17.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"50%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1045.2371113999059,\n        \"min\": 4.0,\n        \"max\": 2822.5,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          22.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"75%\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1333.5181897897007,\n        \"min\": 8.0,\n        \"max\": 3608.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          28.95\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"max\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1896.3035863037785,\n        \"min\": 8.0,\n        \"max\": 5140.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          46.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
              "  <div id=\"df-2ddda6cc-71fa-45d1-b11c-faa3a367f206\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>count</th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "      <th>min</th>\n",
              "      <th>25%</th>\n",
              "      <th>50%</th>\n",
              "      <th>75%</th>\n",
              "      <th>max</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>314.0</td>\n",
              "      <td>23.310510</td>\n",
              "      <td>7.728652</td>\n",
              "      <td>10.0</td>\n",
              "      <td>17.00</td>\n",
              "      <td>22.0</td>\n",
              "      <td>28.95</td>\n",
              "      <td>46.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>314.0</td>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.00</td>\n",
              "      <td>4.0</td>\n",
              "      <td>8.00</td>\n",
              "      <td>8.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>314.0</td>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "      <td>68.0</td>\n",
              "      <td>105.50</td>\n",
              "      <td>151.0</td>\n",
              "      <td>265.75</td>\n",
              "      <td>455.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>314.0</td>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "      <td>46.0</td>\n",
              "      <td>76.25</td>\n",
              "      <td>94.5</td>\n",
              "      <td>128.00</td>\n",
              "      <td>225.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>314.0</td>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "      <td>1649.0</td>\n",
              "      <td>2256.50</td>\n",
              "      <td>2822.5</td>\n",
              "      <td>3608.00</td>\n",
              "      <td>5140.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>314.0</td>\n",
              "      <td>15.559236</td>\n",
              "      <td>2.789230</td>\n",
              "      <td>8.0</td>\n",
              "      <td>13.80</td>\n",
              "      <td>15.5</td>\n",
              "      <td>17.20</td>\n",
              "      <td>24.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model Year</th>\n",
              "      <td>314.0</td>\n",
              "      <td>75.898089</td>\n",
              "      <td>3.675642</td>\n",
              "      <td>70.0</td>\n",
              "      <td>73.00</td>\n",
              "      <td>76.0</td>\n",
              "      <td>79.00</td>\n",
              "      <td>82.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "      display:flex;\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-2ddda6cc-71fa-45d1-b11c-faa3a367f206 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2ddda6cc-71fa-45d1-b11c-faa3a367f206');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "        docLink.innerHTML = docLinkHtml;\n",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-64d90f38-357f-45ed-b749-7a39059a4d51')\"\n",
              "            title=\"Suggest charts\"\n",
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              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
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              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
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              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-64d90f38-357f-45ed-b749-7a39059a4d51 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "              count         mean         std     min      25%     50%  \\\n",
              "MPG           314.0    23.310510    7.728652    10.0    17.00    22.0   \n",
              "Cylinders     314.0     5.477707    1.699788     3.0     4.00     4.0   \n",
              "Displacement  314.0   195.318471  104.331589    68.0   105.50   151.0   \n",
              "Horsepower    314.0   104.869427   38.096214    46.0    76.25    94.5   \n",
              "Weight        314.0  2990.251592  843.898596  1649.0  2256.50  2822.5   \n",
              "Acceleration  314.0    15.559236    2.789230     8.0    13.80    15.5   \n",
              "Model Year    314.0    75.898089    3.675642    70.0    73.00    76.0   \n",
              "\n",
              "                  75%     max  \n",
              "MPG             28.95    46.6  \n",
              "Cylinders        8.00     8.0  \n",
              "Displacement   265.75   455.0  \n",
              "Horsepower     128.00   225.0  \n",
              "Weight        3608.00  5140.0  \n",
              "Acceleration    17.20    24.8  \n",
              "Model Year      79.00    82.0  "
            ]
          },
          "execution_count": 71,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset.describe().transpose()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Db7Auq1yXUvh"
      },
      "source": [
        "### 从标签中分离特征\n",
        "\n",
        "将目标值（“标签”）从特征中分离。此标签是您训练模型来预测的值。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "id": "t2sluJdCW7jN"
      },
      "outputs": [],
      "source": [
        "# 需要转换格式 int 为 float， 否则报 bug\n",
        "# ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type int/float)\n",
        "# ref: https://blog.csdn.net/weixin_41160753/article/details/118001219\n",
        "# ref: https://blog.csdn.net/liveshow021_jxb/article/details/112752145\n",
        "train_features = train_dataset.copy().astype('float64')\n",
        "test_features = test_dataset.copy().astype('float64')\n",
        "\n",
        "train_labels = train_features.pop('MPG')\n",
        "test_labels = test_features.pop('MPG')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mRklxK5s388r"
      },
      "source": [
        "## 归一化\n",
        "\n",
        "在统计信息表中，可以很轻松地看到每个特征的范围的不同："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "IcmY6lKKbkw8",
        "outputId": "eb771832-d22c-4a95-df31-b1695fc91777"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"train_dataset\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"mean\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1105.7051185970897,\n        \"min\": 5.477707006369426,\n        \"max\": 2990.251592356688,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          23.31050955414013,\n          5.477707006369426,\n          15.55923566878981\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"std\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 311.20547986714973,\n        \"min\": 1.6997875727498173,\n        \"max\": 843.8985961905663,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          7.72865199891648,\n          1.6997875727498173,\n          2.789229751888417\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
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              "      <th></th>\n",
              "      <th>mean</th>\n",
              "      <th>std</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>MPG</th>\n",
              "      <td>23.310510</td>\n",
              "      <td>7.728652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cylinders</th>\n",
              "      <td>5.477707</td>\n",
              "      <td>1.699788</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Displacement</th>\n",
              "      <td>195.318471</td>\n",
              "      <td>104.331589</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Horsepower</th>\n",
              "      <td>104.869427</td>\n",
              "      <td>38.096214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Weight</th>\n",
              "      <td>2990.251592</td>\n",
              "      <td>843.898596</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Acceleration</th>\n",
              "      <td>15.559236</td>\n",
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              "    </tr>\n",
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              "      <th>Model Year</th>\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-30913170-e573-4c94-a9f9-13e46607c2bf\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-30913170-e573-4c94-a9f9-13e46607c2bf')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-30913170-e573-4c94-a9f9-13e46607c2bf button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                     mean         std\n",
              "MPG             23.310510    7.728652\n",
              "Cylinders        5.477707    1.699788\n",
              "Displacement   195.318471  104.331589\n",
              "Horsepower     104.869427   38.096214\n",
              "Weight        2990.251592  843.898596\n",
              "Acceleration    15.559236    2.789230\n",
              "Model Year      75.898089    3.675642"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_dataset.describe().transpose()[['mean', 'std']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-ywmerQ6dSox"
      },
      "source": [
        "使用不同的尺度和范围对特征归一化是好的实践。尽管模型*可能* 在没有特征归一化的情况下收敛，它会使得模型训练更加复杂，并会造成生成的模型依赖输入所使用的单位选择。\n",
        "\n",
        "归一化十分重要的一个原因是特征会与模型权重相乘。因此，输出尺度和梯度尺度受输入尺度的影响。\n",
        "\n",
        "尽管模型*可能*在没有特征归一化的情况下收敛，但归一化会使训练更加稳定。\n",
        "\n",
        "注：归一化独热特征没有任何好处，这里这样做是为了简单起见。有关如何使用预处理层的更多详细信息，请参阅[使用预处理层](https://tensorflow.google.cn/guide/keras/preprocessing_layers)指南和[使用 Keras 预处理层对结构化数据进行分类](../structured_data/preprocessing_layers.ipynb)教程。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aFJ6ISropeoo"
      },
      "source": [
        "### 归一化层\n",
        "\n",
        "`tf.keras.layers.Normalization` 是一种将特征归一化添加到模型中的简洁且简单的方法。\n",
        "\n",
        "第一步是创建层："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 172,
      "metadata": {
        "id": "JlC5ooJrgjQF"
      },
      "outputs": [],
      "source": [
        "normalizer = tf.keras.layers.Normalization(axis=-1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 173,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "X12GtlQS8jRI",
        "outputId": "d34e4829-d210-4945-cb68-17d2d4ab613a"
      },
      "outputs": [
        {
          "ename": "AttributeError",
          "evalue": "The layer \"normalization_7\" has never been called and thus has no defined input shape. Note that the `input_shape` property is only available for Functional and Sequential models.",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-173-13321ce3c88e>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnormalizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0minput_shape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/base_layer.py\u001b[0m in \u001b[0;36minput_shape\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m   2116\u001b[0m         \"\"\"\n\u001b[1;32m   2117\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inbound_nodes\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2118\u001b[0;31m             raise AttributeError(\n\u001b[0m\u001b[1;32m   2119\u001b[0m                 \u001b[0;34mf'The layer \"{self.name}\" has never been called '\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2120\u001b[0m                 \u001b[0;34m\"and thus has no defined input shape. Note that the \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: The layer \"normalization_7\" has never been called and thus has no defined input shape. Note that the `input_shape` property is only available for Functional and Sequential models."
          ]
        }
      ],
      "source": [
        "print(normalizer.input_shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XYA2Ap6nVOha"
      },
      "source": [
        "然后，通过调用 `Normalization.adapt` 以将预处理层的状态拟合到数据："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 174,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-Fb3kzo80R2c",
        "outputId": "48d25419-e234-465e-dc89-a5fed5171942"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([[  4.,  90.,  75., ...,   0.,   0.,   1.],\n",
              "       [  4., 140.,  88., ...,   0.,   0.,   1.],\n",
              "       [  8., 350., 160., ...,   0.,   0.,   1.],\n",
              "       ...,\n",
              "       [  4., 108.,  93., ...,   0.,   1.,   0.],\n",
              "       [  4.,  83.,  61., ...,   0.,   1.,   0.],\n",
              "       [  4., 107.,  86., ...,   1.,   0.,   0.]])"
            ]
          },
          "execution_count": 174,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "np.array(train_features)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 175,
      "metadata": {
        "id": "CrBbbjbwV91f"
      },
      "outputs": [],
      "source": [
        "normalizer.adapt(np.array(train_features))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oZccMR5yV9YV"
      },
      "source": [
        "计算均值和方差，并将它们存储在层中。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 79,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GGn-ukwxSPtx",
        "outputId": "8e309d3c-cda4-489e-f987-288e08e950e1"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[   5.478  195.318  104.869 2990.252   15.559   75.898    0.178    0.197\n",
            "     0.624]]\n"
          ]
        }
      ],
      "source": [
        "print(normalizer.mean.numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oGWKaF9GSRuN"
      },
      "source": [
        "当层被调用时，它会返回输入数据，每个特征将单独归一化："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 80,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2l7zFL_XWIRu",
        "outputId": "ea9b7a93-263d-4c9f-ba4f-85ec1d0899bf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First example: [[   4.    90.    75.  2125.    14.5   74.     0.     0.     1. ]]\n",
            "\n",
            "Normalized: [[-0.87 -1.01 -0.79 -1.03 -0.38 -0.52 -0.47 -0.5   0.78]]\n"
          ]
        }
      ],
      "source": [
        "first = np.array(train_features[:1])\n",
        "\n",
        "with np.printoptions(precision=2, suppress=True):\n",
        "  print('First example:', first)\n",
        "  print()\n",
        "  print('Normalized:', normalizer(first).numpy())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6o3CrycBXA2s"
      },
      "source": [
        "## 线性回归\n",
        "\n",
        "在构建深度神经网络模型之前，首先使用一个和多个变量进行线性回归。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lFby9n0tnHkw"
      },
      "source": [
        "### 使用一个变量进行线性回归\n",
        "\n",
        "从单变量线性回归开始，根据 `'Horsepower'` 预测 `'MPG'`。\n",
        "\n",
        "使用 `tf.keras` 训练模型通常从定义模型架构开始。使用 `tf.keras.Sequential` 模型，它[表示一系列步骤](https://tensorflow.google.cn/guide/keras/sequential_model)。\n",
        "\n",
        "单变量线性回归模型有两个步骤：\n",
        "\n",
        "- 使用 `tf.keras.layers.Normalization` 预处理层规一化 `'Horsepower'` 输入特征。\n",
        "- 应用线性变换 ($y = mx+b$) 以使用线性层 (`tf.keras.layers.Dense`) 生成 1 个输出。\n",
        "\n",
        "*输入*的数量可以由 `input_shape` 参数设置，也可以在模型第一次运行时自动设置。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp3gAFn3TPv8"
      },
      "source": [
        "首先，创建一个由 `'Horsepower'` 特征构成的 NumPy 数组。然后，实例化 `tf.keras.layers.Normalization` 并将其状态拟合到 `horsepower` 数据："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 81,
      "metadata": {
        "id": "1gJAy0fKs1TS"
      },
      "outputs": [],
      "source": [
        "horsepower = np.array(train_features['Horsepower'])\n",
        "\n",
        "horsepower_normalizer = layers.Normalization(input_shape=[1,], axis=None)\n",
        "horsepower_normalizer.adapt(horsepower)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4NVlHJY2TWlC"
      },
      "source": [
        "构建 Keras 序贯模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 82,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0sXM7qLlKfZ",
        "outputId": "e2e25956-1825-400e-af9e-938c48959cf6"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization_3 (Normaliza  (None, 1)                 3         \n",
            " tion)                                                           \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 1)                 2         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5 (24.00 Byte)\n",
            "Trainable params: 2 (8.00 Byte)\n",
            "Non-trainable params: 3 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "horsepower_model = tf.keras.Sequential([\n",
        "    horsepower_normalizer,\n",
        "    layers.Dense(units=1)\n",
        "])\n",
        "\n",
        "horsepower_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eObQu9fDnXGL"
      },
      "source": [
        "此模型将根据 `'Horsepower'` 预测 `'MPG'`。\n",
        "\n",
        "在前 10 个“马力”值上运行未经训练的模型。输出不会很好，但您会看到它具有预期的形状 `(10, 1)`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 83,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UfV1HS6bns-s",
        "outputId": "81293273-8d1c-426b-876e-30bf91e107c0"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 85ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[ 0.903],\n",
              "       [ 0.51 ],\n",
              "       [-1.667],\n",
              "       [ 1.266],\n",
              "       [ 1.145],\n",
              "       [ 0.449],\n",
              "       [ 1.356],\n",
              "       [ 1.145],\n",
              "       [ 0.298],\n",
              "       [ 0.51 ]], dtype=float32)"
            ]
          },
          "execution_count": 83,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "horsepower_model.predict(horsepower[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CSkanJlmmFBX"
      },
      "source": [
        "构建模型后，使用 Keras `Model.compile` 方法配置训练过程。要编译的最重要参数是 `loss` 和 `optimizer`，因为它们定义了将要优化的内容 (`mean_absolute_error`) 以及优化的方法（使用 `tf.keras.optimizers.Adam`）。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 84,
      "metadata": {
        "id": "JxA_3lpOm-SK"
      },
      "outputs": [],
      "source": [
        "horsepower_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "    loss='mean_absolute_error')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Z3q1I9TwnRSC"
      },
      "source": [
        "使用 Keras `Model.fit` 执行 100 个周期的训练："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 85,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-iSrNy59nRAp",
        "outputId": "4d25d9be-39b0-4206-dac0-380b6b820712"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 5.01 s, sys: 296 ms, total: 5.31 s\n",
            "Wall time: 5.7 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = horsepower_model.fit(\n",
        "    train_features['Horsepower'],\n",
        "    train_labels,\n",
        "    epochs=100,\n",
        "    # Suppress logging.\n",
        "    verbose=0,\n",
        "    # Calculate validation results on 20% of the training data.\n",
        "    validation_split = 0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQm3pc0FYPQB"
      },
      "source": [
        "使用 `history` 对象中存储的统计信息呈现模型的训练进度："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 86,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "YCAwD_y4AdC3",
        "outputId": "1f2658f0-4b8d-443a-ebf2-7fe559fa077d"
      },
      "outputs": [
        {
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              "      <th>loss</th>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "    })();\n",
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              "</div>\n",
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              "  </div>\n"
            ],
            "text/plain": [
              "        loss  val_loss  epoch\n",
              "95  3.805278  4.159013     95\n",
              "96  3.810045  4.191255     96\n",
              "97  3.803530  4.191302     97\n",
              "98  3.803053  4.195515     98\n",
              "99  3.803777  4.195645     99"
            ]
          },
          "execution_count": 86,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "hist = pd.DataFrame(history.history)\n",
        "hist['epoch'] = history.epoch\n",
        "hist.tail()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 87,
      "metadata": {
        "id": "9E54UoZunqhc"
      },
      "outputs": [],
      "source": [
        "def plot_loss(history):\n",
        "  plt.plot(history.history['loss'], label='loss')\n",
        "  plt.plot(history.history['val_loss'], label='val_loss')\n",
        "  plt.ylim([0, 10])\n",
        "  plt.xlabel('Epoch')\n",
        "  plt.ylabel('Error [MPG]')\n",
        "  plt.legend()\n",
        "  plt.grid(True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 88,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "yYsQYrIZyqjz",
        "outputId": "dccbf0f2-b3b2-458d-a8f9-4c79542c0061"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CMNrt8X2ebXd"
      },
      "source": [
        "收集测试集上的结果，供后面使用："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 89,
      "metadata": {
        "id": "kDZ8EvNYrDtx"
      },
      "outputs": [],
      "source": [
        "test_results = {}\n",
        "\n",
        "test_results['horsepower_model'] = horsepower_model.evaluate(\n",
        "    test_features['Horsepower'],\n",
        "    test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F0qutYAKwoda"
      },
      "source": [
        "由于这是一个单变量回归，很容易将模型的预测视为输入的函数："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 90,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xDS2JEtOn9Jn",
        "outputId": "5ce88237-fc00-443e-c666-ca64664cfd23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "x = tf.linspace(0.0, 250, 251)\n",
        "y = horsepower_model.predict(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 91,
      "metadata": {
        "id": "rttFCTU8czsI"
      },
      "outputs": [],
      "source": [
        "def plot_horsepower(x, y):\n",
        "  plt.scatter(train_features['Horsepower'], train_labels, label='Data')\n",
        "  plt.plot(x, y, color='k', label='Predictions')\n",
        "  plt.xlabel('Horsepower')\n",
        "  plt.ylabel('MPG')\n",
        "  plt.legend()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 92,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "7l9ZiAOEUNBL",
        "outputId": "c4a672e2-d72a-4602-d634-0b0dd3f2114b"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_horsepower(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yk2RmlqPoM9u"
      },
      "source": [
        "### 使用多个输入进行线性回归"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PribnwDHUksC"
      },
      "source": [
        "您可以使用几乎相同的设置根据多个输入进行预测。此模型仍然执行相同的 $y = mx+b$，只是 $m$ 是一个矩阵，而 $b$ 是一个向量。\n",
        "\n",
        "再次创建一个两步 Keras 序贯模型，第一层为您之前定义并拟合到整个数据集的 `normalizer` (`tf.keras.layers.Normalization(axis=-1)`)："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 93,
      "metadata": {
        "id": "ssnVcKg7oMe6"
      },
      "outputs": [],
      "source": [
        "linear_model = tf.keras.Sequential([\n",
        "    normalizer,\n",
        "    layers.Dense(units=1)\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IHlx6WeIWyAr"
      },
      "source": [
        "当您对一批输入调用 `Model.predict` 时，它会为每个样本生成 `units=1` 输出。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 94,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DynfJV18WiuT",
        "outputId": "9616f2d9-e2ef-471b-b400-fa5125f42f34"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "1/1 [==============================] - 0s 39ms/step\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "array([[ 0.731],\n",
              "       [ 0.527],\n",
              "       [-1.357],\n",
              "       [ 1.369],\n",
              "       [ 0.342],\n",
              "       [ 0.13 ],\n",
              "       [ 0.444],\n",
              "       [ 0.801],\n",
              "       [-0.028],\n",
              "       [-0.415]], dtype=float32)"
            ]
          },
          "execution_count": 94,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "linear_model.predict(train_features[:10])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvHKH3rPXHmq"
      },
      "source": [
        "当您调用模型时，将构建其权重矩阵 – 可以看到 `kernel` 权重（$y=mx+b$ 中的 $m$）的形状为 `(9, 1)`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 95,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DwJ4Fq0RXBQf",
        "outputId": "5d6830b9-bbcb-41a9-e8ed-9380d0ff733f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<tf.Variable 'dense_9/kernel:0' shape=(9, 1) dtype=float32, numpy=\n",
              "array([[ 0.124],\n",
              "       [-0.411],\n",
              "       [-0.251],\n",
              "       [-0.242],\n",
              "       [-0.092],\n",
              "       [ 0.287],\n",
              "       [ 0.539],\n",
              "       [ 0.125],\n",
              "       [ 0.521]], dtype=float32)>"
            ]
          },
          "execution_count": 95,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "linear_model.layers[1].kernel"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eINAc6rZXzOt"
      },
      "source": [
        "使用 Keras `Model.compile` 配置模型并使用 `Model.fit` 训练 100 个周期："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 96,
      "metadata": {
        "id": "A0Sv_Ybr0szp"
      },
      "outputs": [],
      "source": [
        "linear_model.compile(\n",
        "    optimizer=tf.keras.optimizers.Adam(learning_rate=0.1),\n",
        "    loss='mean_absolute_error')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 97,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EZoOYORvoTSe",
        "outputId": "052a6410-f8b9-4eaa-c453-31f36366163c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 4.71 s, sys: 270 ms, total: 4.98 s\n",
            "Wall time: 4.31 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = linear_model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    epochs=100,\n",
        "    # Suppress logging.\n",
        "    verbose=0,\n",
        "    # Calculate validation results on 20% of the training data.\n",
        "    validation_split = 0.2)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdxiCbiNYK2F"
      },
      "source": [
        "使用此回归模型中的所有输入可以实现比 `horsepower_model`（具有一个输入）低得多的训练和验证误差："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 98,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "4sWO3W0koYgu",
        "outputId": "d285393e-20cf-428f-ab18-1f567e23e737"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NyN49hIWe_NH"
      },
      "source": [
        "收集测试集上的结果，供后面使用："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 99,
      "metadata": {
        "id": "jNC3D1DGsGgK"
      },
      "outputs": [],
      "source": [
        "test_results['linear_model'] = linear_model.evaluate(\n",
        "    test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SmjdzxKzEu1-"
      },
      "source": [
        "## 使用深度神经网络 (DNN) 进行回归"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DT_aHPsrzO1t"
      },
      "source": [
        "在上一部分中，您为单输入和多输入实现了两个线性模型。\n",
        "\n",
        "在此，您将实现单输入和多输入 DNN 模型。\n",
        "\n",
        "除了将模型扩展为包括一些“隐藏”非线性层之外，代码基本相同。此处的名称“隐藏”仅表示不直接连接到输入或输出。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6SWtkIjhrZwa"
      },
      "source": [
        "这些模型包含的层比线性模型多一些：\n",
        "\n",
        "- 归一化层和以前一样（对于单输入模型使用 `horsepower_normalizer`，对于多输入模型使用 `normalizer`）。\n",
        "- 使用 ReLU (`relu`) 激活函数非线性的两个隐藏非线性 `Dense` 层。\n",
        "- 一个线性 `Dense` 单输出层。\n",
        "\n",
        "两个模型都将使用相同的训练过程，因此 `compile` 方法包含在下面的 `build_and_compile_model` 函数中。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 194,
      "metadata": {
        "id": "c26juK7ZG8j-"
      },
      "outputs": [],
      "source": [
        "def build_and_compile_model(norm):\n",
        "  model = keras.Sequential([\n",
        "      norm,\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(64, activation='relu'),\n",
        "      layers.Dense(1)\n",
        "  ])\n",
        "\n",
        "  model.compile(loss='mean_absolute_error',\n",
        "                optimizer=tf.keras.optimizers.Adam(0.001))\n",
        "  print(model.input_shape)\n",
        "  return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6c51caebbc0d"
      },
      "source": [
        "### 使用 DNN 和单输入进行回归"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xvu9gtxTZR5V"
      },
      "source": [
        "创建一个 DNN 模型，仅将 `'Horsepower'` 作为输入，将 `'Horsepower'`（之前定义）作为归一化层："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 195,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cGbPb-PHGbhs",
        "outputId": "72a80fde-a5fb-412b-ff48-268a94783fc4"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(None, 1)\n"
          ]
        }
      ],
      "source": [
        "dnn_horsepower_model = build_and_compile_model(horsepower_normalizer)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Sj49Og4YGULr"
      },
      "source": [
        "此模型比线性模型的可训练参数多很多。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 196,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ReAD0n6MsFK-",
        "outputId": "b4a6903b-6878-4ad3-e332-d8ac963f4e60"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization_3 (Normaliza  (None, 1)                 3         \n",
            " tion)                                                           \n",
            "                                                                 \n",
            " dense_34 (Dense)            (None, 64)                128       \n",
            "                                                                 \n",
            " dense_35 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_36 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4356 (17.02 KB)\n",
            "Trainable params: 4353 (17.00 KB)\n",
            "Non-trainable params: 3 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn_horsepower_model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0-qWCsh6DlyH"
      },
      "source": [
        "使用 Keras `Model.fit` 训练模型："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 197,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sD7qHCmNIOY0",
        "outputId": "dcbf0757-9175-4918-8e53-c4ce71cd2e94"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 5.56 s, sys: 334 ms, total: 5.9 s\n",
            "Wall time: 5.22 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = dnn_horsepower_model.fit(\n",
        "    train_features['Horsepower'],\n",
        "    train_labels,\n",
        "    validation_split=0.2,\n",
        "    verbose=0, epochs=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dArGGxHxcKjN"
      },
      "source": [
        "此模型略优于线性单输入 `horsepower_model`："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 198,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "NcF6UWjdCU8T",
        "outputId": "b3c1f82e-7531-433c-900c-eee0c7e1ec44"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TG1snlpR2QCK"
      },
      "source": [
        "如果您将预测值绘制为 `'Horsepower'` 的函数，应看到此模型如何利用隐藏层提供的非线性："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 199,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hPF53Rem14NS",
        "outputId": "2edc2d6b-166a-489e-ae48-773c01e20524"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8/8 [==============================] - 0s 2ms/step\n"
          ]
        }
      ],
      "source": [
        "x = tf.linspace(0.0, 250, 251)\n",
        "y = dnn_horsepower_model.predict(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 200,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "rsf9rD8I17Wq",
        "outputId": "49f14922-d8f4-4367-d7bc-2dcb350a5b54"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_horsepower(x, y)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WxCJKIUpe4io"
      },
      "source": [
        "收集测试集上的结果，供后面使用："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 201,
      "metadata": {
        "id": "bJjM0dU52XtN"
      },
      "outputs": [],
      "source": [
        "test_results['dnn_horsepower_model'] = dnn_horsepower_model.evaluate(\n",
        "    test_features['Horsepower'], test_labels,\n",
        "    verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S_2Btebp2e64"
      },
      "source": [
        "### 使用 DNN 和多输入进行回归"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aKFtezDldLSf"
      },
      "source": [
        "使用所有输入重复前面的过程。模型的性能在验证数据集上会略有提高。"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 211,
      "metadata": {
        "id": "XhHB6p8AER1d"
      },
      "outputs": [],
      "source": [
        "# if not set argument: input_dim=9, when load this model after save it, bug!\n",
        "# ValueError: All `axis` values to be kept must have known shape. Got axis: (-1,), input shape: [None, None], with unknown axis at index: 1\n",
        "# ref: https://github.com/keras-team/keras/issues/15348\n",
        "normalizer = tf.keras.layers.Normalization(axis=None, input_dim=9)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 212,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c0mhscXh2k36",
        "outputId": "ba36c4d2-8322-4daa-d2c0-56e0fd83028f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(None, 9)\n",
            "Model: \"sequential_16\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " normalization_16 (Normaliz  (None, 9)                 3         \n",
            " ation)                                                          \n",
            "                                                                 \n",
            " dense_40 (Dense)            (None, 64)                640       \n",
            "                                                                 \n",
            " dense_41 (Dense)            (None, 64)                4160      \n",
            "                                                                 \n",
            " dense_42 (Dense)            (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 4868 (19.02 KB)\n",
            "Trainable params: 4865 (19.00 KB)\n",
            "Non-trainable params: 3 (16.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "dnn_model = build_and_compile_model(normalizer)\n",
        "dnn_model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 213,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CXDENACl2tuW",
        "outputId": "ba7e38ff-b4b2-42dd-93fc-d9f42c904d53"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 5.5 s, sys: 315 ms, total: 5.82 s\n",
            "Wall time: 5.61 s\n"
          ]
        }
      ],
      "source": [
        "%%time\n",
        "history = dnn_model.fit(\n",
        "    train_features,\n",
        "    train_labels,\n",
        "    validation_split=0.2,\n",
        "    verbose=0, epochs=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 214,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 455
        },
        "id": "-9Dbj0fX23RQ",
        "outputId": "9a2c25d7-125e-4208-d8d3-2a472af177e9"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "plot_loss(history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hWoVYS34fJPZ"
      },
      "source": [
        "收集测试集上的结果："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 215,
      "metadata": {
        "id": "-bZIa96W3c7K"
      },
      "outputs": [],
      "source": [
        "test_results['dnn_model'] = dnn_model.evaluate(test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uiCucdPLfMkZ"
      },
      "source": [
        "## 性能"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rDf1xebEfWBw"
      },
      "source": [
        "所有模型都已经过训练，因此您可以查看它们的测试集性能："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 216,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "id": "e5_ooufM5iH2",
        "outputId": "00d89508-d640-4cca-904a-6d1ba3103768"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
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            "text/plain": [
              "                      Mean absolute error [MPG]\n",
              "horsepower_model                       3.658166\n",
              "linear_model                           2.466898\n",
              "dnn_horsepower_model                   2.916475\n",
              "dnn_model                              6.696378"
            ]
          },
          "execution_count": 216,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DABIVzsCf-QI"
      },
      "source": [
        "这些结果与训练期间看到的验证误差相匹配。"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ft603OzXuEZC"
      },
      "source": [
        "### 做预测\n",
        "\n",
        "您现在可以使用 Keras `Model.predict`，在测试集上利用 `dnn_model` 进行预测并查看损失："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 217,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "Xe7RXH3N3CWU",
        "outputId": "81788862-9d59-4847-8bc6-6506cb6c1d46"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "3/3 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "test_predictions = dnn_model.predict(test_features).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True Values [MPG]')\n",
        "plt.ylabel('Predictions [MPG]')\n",
        "lims = [0, 50]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims, lims)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "19wyogbOSU5t"
      },
      "source": [
        "看起来模型预测得相当出色。\n",
        "\n",
        "现在，查看一下误差分布："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 218,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "f-OHX4DiXd8x",
        "outputId": "e0800911-5da3-4eac-d2a9-c49e15adc28d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "error = test_predictions - test_labels\n",
        "plt.hist(error, bins=25)\n",
        "plt.xlabel('Prediction Error [MPG]')\n",
        "_ = plt.ylabel('Count')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KSyaHUfDT-mZ"
      },
      "source": [
        "如果您对模型感到满意，请使用 `Model.save` 将其保存以备后续使用："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 220,
      "metadata": {
        "id": "4-WwLlmfT-mb"
      },
      "outputs": [],
      "source": [
        "dnn_model.save('dnn_model.keras', save_format='tf')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Benlnl8UT-me"
      },
      "source": [
        "如果您重新加载模型，它会给出相同的输出："
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 221,
      "metadata": {
        "id": "dyyyj2zVT-mf"
      },
      "outputs": [],
      "source": [
        "reloaded = tf.keras.models.load_model('dnn_model.keras')\n",
        "\n",
        "test_results['reloaded'] = reloaded.evaluate(\n",
        "    test_features, test_labels, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 222,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "f_GchJ2tg-2o",
        "outputId": "a2f086dc-1168-40a0-c85a-0ccf7cf966f9"
      },
      "outputs": [
        {
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "summary": "{\n  \"name\": \"pd\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"Mean absolute error [MPG]\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.0613728828312436,\n        \"min\": 2.46689772605896,\n        \"max\": 6.696378231048584,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          2.46689772605896,\n          6.696378231048584,\n          3.6581664085388184\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}",
              "type": "dataframe"
            },
            "text/html": [
              "\n",
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              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
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              "        vertical-align: top;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Mean absolute error [MPG]</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>horsepower_model</th>\n",
              "      <td>3.658166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>linear_model</th>\n",
              "      <td>2.466898</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dnn_horsepower_model</th>\n",
              "      <td>2.916475</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>dnn_model</th>\n",
              "      <td>6.696378</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>reloaded</th>\n",
              "      <td>6.696378</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    <div class=\"colab-df-buttons\">\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-a8568ac6-3d8d-4265-8d17-72297fd5b350 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-a8568ac6-3d8d-4265-8d17-72297fd5b350');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-4a6d592e-e1a8-4357-94ee-e7701c175712\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-4a6d592e-e1a8-4357-94ee-e7701c175712')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-4a6d592e-e1a8-4357-94ee-e7701c175712 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "text/plain": [
              "                      Mean absolute error [MPG]\n",
              "horsepower_model                       3.658166\n",
              "linear_model                           2.466898\n",
              "dnn_horsepower_model                   2.916475\n",
              "dnn_model                              6.696378\n",
              "reloaded                               6.696378"
            ]
          },
          "execution_count": 222,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "pd.DataFrame(test_results, index=['Mean absolute error [MPG]']).T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vgGQuV-yqYZH"
      },
      "source": [
        "## 结论\n",
        "\n",
        "本笔记本 (notebook) 介绍了一些处理回归问题的技术。\n",
        "\n",
        "- 均方误差 (MSE) (`tf.keras.losses.MeanSquaredError`) 和平均绝对误差 (MAE) (`tf.keras.losses.MeanAbsoluteError`) 是用于回归问题的常见损失函数。MAE 对异常值不那么敏感。不同的损失函数用于分类问题。\n",
        "- 类似的，用于回归的评估指标与分类不同。 常见的回归指标是平均绝对误差（MAE）。\n",
        "- 当数字输入数据特征的值存在不同范围时，每个特征应独立缩放到相同范围。\n",
        "- 过拟合是 DNN 模型的常见问题，但本教程不存在此问题。有关这方面的更多帮助，请访问[过拟合和欠拟合](overfit_and_underfit.ipynb)教程。"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "gpuType": "T4",
      "name": "regression.ipynb",
      "provenance": [],
      "toc_visible": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
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